Utility of digital radiography for the screening of pneumoconiosis as compared to analog radiography: radiation dose, image quality, and pneumoconiosis classification

Early Detection Methods for Silicosis in Australia and Internationally: A Review of the Literature

Pneumoconiosis, or occupational lung disease, is one of the world’s most prevalent work-related diseases. Silicosis, a type of pneumoconiosis, is caused by inhaling respirable crystalline silica (RCS) dust. Although silicosis can be fatal, it is completely preventable. Hundreds of thousands of workers globally are at risk of being exposed to RCS at the workplace from various activities in many industries. Currently, in Australia and internationally, there are a range of methods used for the respiratory surveillance of workers exposed to RCS. These methods include health and exposure questionnaires, spirometry, chest X-rays, and HRCT. However, these methods predominantly do not detect the disease until it has significantly progressed. For this reason, there is a growing body of research investigating early detection methods for silicosis, particularly biomarkers. This literature review summarises the research to date on early detection methods for silicosis and makes recommendations for future work in this area. Findings from this review conclude that there is a critical need for an early detection method for silicosis, however, further laboratory- and field-based research is required.

Potential of deep learning in assessing pneumoconiosis depicted on digital chest radiography

Objectives

To investigate the potential of deep learning in assessing pneumoconiosis depicted on digital chest radiographs and to compare its performance with certified radiologists.

Methods

We retrospectively collected a dataset consisting of 1881 chest X-ray images in the form of digital radiography. These images were acquired in a screening setting on subjects who had a history of working in an environment that exposed them to harmful dust. Among these subjects, 923 were diagnosed with pneumoconiosis, and 958 were normal. To identify the subjects with pneumoconiosis, we applied a classical deep convolutional neural network (CNN) called Inception-V3 to these image sets and validated the classification performance of the trained models using the area under the receiver operating characteristic curve (AUC). In addition, we asked two certified radiologists to independently interpret the images in the testing dataset and compared their performance with the computerised scheme.

Results

The Inception-V3 CNN architecture, which was trained on the combination of the three image sets, achieved an AUC of 0.878 (95% CI 0.811 to 0.946). The performance of the two radiologists in terms of AUC was 0.668 (95% CI 0.555 to 0.782) and 0.772 (95% CI 0.677 to 0.866), respectively. The agreement between the two readers was moderate (kappa: 0.423, p<0.001).

Conclusion

Our experimental results demonstrated that the deep leaning solution could achieve a relatively better performance in classification as compared with other models and the certified radiologists, suggesting the feasibility of deep learning techniques in screening pneumoconiosis.

Imaging in occupational and environmental lung disease

PURPOSE OF REVIEW

Work-related and environmental lung disease is increasing over time and new antigens causing pulmonary response are described in medical literature every year with more or less specific imaging findings. The purposes of this review are to highlight the current role of imaging, describe classic as well as uncommon high-resolution computed tomography (HRCT) patterns helpful in guiding diagnosis and update the recent literature on this topic.

RECENT FINDINGS

Recent literature on imaging of occupational and environmental lung disease is scarce and is predominantly based on the HRCT appearance related to specific antigen exposure and on prognostic significance of findings.

SUMMARY

Knowledge of imaging modality potential and radiologic appearance may guide identification, characterization and follow-up of old and new occupational and environmental lung diseases. HRCT plays a key role in this context, having radiation exposure as a major limitation, especially in asymptomatic patients. Low-dose HRCT has the potential to be employed for screening and surveillance of the exposed individuals. However, future research is needed to further this field.

Reliability and validity of soft copy images based on flat-panel detector in pneumoconiosis classification: comparison with the analog radiographs

RATIONALE AND OBJECTIVES

The aim of this study was to evaluate the reliability and validity of soft copy images based on flat-panel detector of digital radiography (DR-FPD soft copy images) compared to analog radiographs (ARs) in pneumoconiosis classification and diagnosis.

MATERIALS AND METHODS

DR-FPD soft copy images and ARs from 349 subjects were independently read by four-experienced readers according to the International Labor Organization 2000 guidelines. DR-FPD soft copy images were used to obtain consensus reading (CR) by all readers as the gold standard. Reliability and validity were evaluated by a κ and receiver operating characteristic analysis, respectively.

RESULTS

In small opacity, overall interreader agreement of DR-FPD soft copy images was significantly higher than that of ARs, but it was not significantly different in large opacity and costophrenic angle obliteration. In small opacity, agreement of DR-FPD soft copy images with CR was significantly higher than that of ARs with CR. It was also higher than that of ARs with CR in pleural plaque and thickening. Receiver operating characteristic areas were not different significantly between DR-FPD soft copy images and ARs.

CONCLUSIONS

DR-FPD soft copy images showed accurate and reliable results in pneumoconiosis classification and diagnosis compared to ARs.